Perception Engineer, Machine Learning

Anduril Anduril · Defense · Costa Mesa, CA, Seattle, WA · Air Dominance & Strike : Mission Autonomy Engineering : MFX Core Robotics

Machine Learning Engineer focused on developing and deploying production perception systems for autonomous vehicles (UAVs, USVs, UGVs) in the defense industry. This role involves end-to-end ownership of perception capabilities, from data preparation and model design to deployment, monitoring, and continuous improvement on embedded hardware with real-time constraints. Requires strong experience in computer vision, ML model optimization, production deployment, and CI/CD pipelines.

What you'd actually do

  1. Own perception capabilities end-to-end including detection, classification, segmentation, depth estimation, scene description, tracking, sensor fusion, and calibration across our UAV, USV, and UGV platforms .
  2. Bring state-of-the-art models into production, survey, prototype, and integrate the latest research while ensuring deployments are maintainable and observable in the field .
  3. Optimize and deploy models to embedded hardware such as NVIDIA Jetson/Orin-class platforms, meeting real-time latency budgets on the order of 30 ms per frame .
  4. Architect and improve our CI/CD pipelines. We use systems to continuously ingest data, label, and train new models. You would have a direct role in shaping these systems.
  5. Onboard new sensor modalities and features end-to-end, from cradle to grave, whether that's adding OCR metadata extraction to our pipeline or integrating a new LiDAR point cloud source. Example sensors includes EO, IR, stereo, radar, and LiDAR .

Skills

Required

  • Python
  • C++
  • Rust
  • PyTorch
  • OpenCV
  • TensorFlow
  • model optimization
  • embedded inference
  • quantization
  • pruning
  • TensorRT
  • ONNX
  • classical computer vision
  • camera calibration
  • homography
  • multi-view geometry
  • feature-based methods
  • CI/CD practices
  • automated tests
  • ML pipelines
  • U.S. Citizen

Nice to have

  • Master's degree or higher
  • PhD
  • SLAM
  • AWS training infrastructure
  • on-prem GPU clusters
  • Weights & Biases
  • Flyte
  • RunAI
  • synthetic data generation
  • sim-to-real workflows
  • CUDA
  • GPU programming
  • defense
  • aerospace
  • autonomous systems

What the JD emphasized

  • production perception systems
  • autonomous Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vessels (USVs), and Unmanned Ground Vehicles (UGVs)
  • push state-of-the-art perception models into the field
  • maintainable, observable, and reliable once they're deployed on hardware
  • own perception capabilities end-to-end
  • optimize and deploy models to embedded hardware
  • real-time latency budgets
  • CI/CD pipelines
  • continuously ingest data, label, and train new models
  • onboard new sensor modalities
  • train models in the cloud and on-prem
  • monitor deployed models for performance divergence
  • build the tooling needed to detect and respond to drift in the field
  • ship perception capabilities into production systems
  • 5+ years of industry experience building and deploying perception or computer vision systems in production
  • Robotics experience, you have shipped perception software onto real robotic platforms
  • Python for model development and training
  • production experience in C++ and/or Rust for real-time, on-hardware deployment
  • PyTorch and OpenCV
  • TensorFlow experience
  • track record of designing, refining, and deploying ML models into production
  • ownership of maintainability and field monitoring
  • Model optimization for embedded inference, quantization, pruning, TensorRT, and ONNX
  • classical computer vision
  • camera calibration, homography, multi-view geometry, and feature-based methods
  • structured and unstructured data
  • integrating new sensor modalities
  • CI/CD practices
  • automated tests around ML pipelines
  • End-to-end ownership mindset
  • maintain what you build and design it to be maintainable
  • Cross-team collaboration skills
  • U.S. Citizen and eligible to obtain and maintain a U.S. security clearance
  • Experience with SLAM
  • Experience with AWS training infrastructure, on-prem GPU clusters, and tools such as Weights & Biases, Flyte, and RunAI
  • Experience with synthetic data generation and sim-to-real workflows
  • CUDA / GPU programming experience
  • Background in defense, aerospace, or autonomous systems

Other signals

  • production perception systems
  • autonomous Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vessels (USVs), and Unmanned Ground Vehicles (UGVs)
  • push state-of-the-art perception models into the field
  • maintainable, observable, and reliable once they're deployed on hardware
  • own perception capabilities end-to-end
  • optimize and deploy models to embedded hardware
  • real-time latency budgets
  • CI/CD pipelines
  • continuously ingest data, label, and train new models
  • onboard new sensor modalities
  • train models in the cloud and on-prem
  • monitor deployed models for performance divergence
  • build the tooling needed to detect and respond to drift in the field
  • ship perception capabilities into production systems
  • 5+ years of industry experience building and deploying perception or computer vision systems in production
  • Robotics experience, you have shipped perception software onto real robotic platforms
  • Python for model development and training
  • production experience in C++ and/or Rust for real-time, on-hardware deployment
  • PyTorch and OpenCV
  • TensorFlow experience
  • track record of designing, refining, and deploying ML models into production
  • ownership of maintainability and field monitoring
  • Model optimization for embedded inference, quantization, pruning, TensorRT, and ONNX
  • classical computer vision
  • camera calibration, homography, multi-view geometry, and feature-based methods
  • structured and unstructured data
  • integrating new sensor modalities
  • CI/CD practices
  • automated tests around ML pipelines
  • End-to-end ownership mindset
  • maintain what you build and design it to be maintainable
  • Cross-team collaboration skills
  • U.S. Citizen and eligible to obtain and maintain a U.S. security clearance
  • Experience with SLAM
  • Experience with AWS training infrastructure, on-prem GPU clusters, and tools such as Weights & Biases, Flyte, and RunAI
  • Experience with synthetic data generation and sim-to-real workflows
  • CUDA / GPU programming experience
  • Background in defense, aerospace, or autonomous systems